内容简介：首先保证在一个IP为192.168.1.100的机器上启动ps或worker进程：因为该进程启动位置是192.168.1.100，但是运行参数中指定的task_index为1，对应的IP地址是ps_hosts或worker_hosts的第二项（第一项的task_index为0)，也就是192.168.1.101，和进程本身所在机器的IP不一致。
2018-12-05 22:18:24.565303: I tensorflow/core/distributed_runtime/rpc/grpc_channel.cc:222] Initialize GrpcChannelCache for job ps -> {0 -> localhost:3376}
2018-12-05 22:18:24.565372: I tensorflow/core/distributed_runtime/rpc/grpc_channel.cc:222] Initialize GrpcChannelCache for job worker -> {0 -> localhost:3330, 1 -> localhost:3331}
2018-12-05 22:18:24.569212: I tensorflow/core/distributed_runtime/rpc/grpc_server_lib.cc:381] Started server with target: grpc://localhost:3376
2018-12-05 22:18:26.170901: I tensorflow/core/distributed_runtime/rpc/grpc_channel.cc:222] Initialize GrpcChannelCache for job ps -> {0 -> localhost:3376}
2018-12-05 22:18:26.170969: I tensorflow/core/distributed_runtime/rpc/grpc_channel.cc:222] Initialize GrpcChannelCache for job worker -> {0 -> localhost:3330, 1 -> localhost:3331}
2018-12-05 22:18:26.174856: I tensorflow/core/distributed_runtime/rpc/grpc_server_lib.cc:381] Started server with target: grpc://localhost:3330
2018-12-05 22:18:27.177003: I tensorflow/core/distributed_runtime/rpc/grpc_channel.cc:222] Initialize GrpcChannelCache for job ps -> {0 -> localhost:3376}
2018-12-05 22:18:27.177071: I tensorflow/core/distributed_runtime/rpc/grpc_channel.cc:222] Initialize GrpcChannelCache for job worker -> {0 -> localhost:3330, 1 -> localhost:3331}
2018-12-05 22:18:27.180980: I tensorflow/core/distributed_runtime/rpc/grpc_server_lib.cc:381] Started server with target: grpc://localhost:3331
2018-12-05 22:18:34.625459: I tensorflow/core/distributed_runtime/master.cc:267] CreateSession still waiting for response from worker: /job:worker/replica:0/task:0
2018-12-05 22:18:34.625513: I tensorflow/core/distributed_runtime/master.cc:267] CreateSession still waiting for response from worker: /job:worker/replica:0/task:1
2018-12-05 22:18:36.231936: I tensorflow/core/distributed_runtime/master.cc:267] CreateSession still waiting for response from worker: /job:ps/replica:0/task:0
2018-12-05 22:18:36.231971: I tensorflow/core/distributed_runtime/master.cc:267] CreateSession still waiting for response from worker: /job:worker/replica:0/task:1
2018-12-05 22:18:37.235899: I tensorflow/core/distributed_runtime/master.cc:267] CreateSession still waiting for response from worker: /job:ps/replica:0/task:0
2018-12-05 22:18:37.235952: I tensorflow/core/distributed_runtime/master.cc:267] CreateSession still waiting for response from worker: /job:worker/replica:0/task:0
首先保证 job_name,task_index,ps_hosts,worker_hosts
这四个参数都是正确的,考虑以下这种情况是不正确的：
在一个IP为192.168.1.100的机器上启动ps或worker进程：
--job_name=worker --task_index=1 --ps_hosts=192.168.1.100:2222,192.168.1.101:2222 --worker_hosts=192.168.1.100:2223,192.168.1.101:2223
因为该进程启动位置是192.168.1.100，但是运行参数中指定的task_index为1，对应的IP地址是ps_hosts或worker_hosts的第二项（第一项的task_index为0)，也就是192.168.1.101，和进程本身所在机器的IP不一致。
另外一种情况也会导致该问题的发生，从TensorFlow-1.4开始，分布式会自动使用环境变量中的代理去连接，如果运行的节点之间不需要代理互连，那么将代理的环境变量移除即可，在脚本的开始位置添加代码：
注意这段代码必须写在import tensorflow as tf或者import moxing.tensorflow as mox之前
import os
os.enrivon.pop('http_proxy')
os.enrivon.pop('https_proxy')
— 摘自( https://bbs.huaweicloud.com/blogs/463145f7a1d111e89fc57ca23e93a89f )
以上所述就是小编给大家介绍的《Tensorflow 错误集锦》，希望对大家有所帮助，如果大家有任何疑问请给我留言，小编会及时回复大家的。在此也非常感谢大家对 码农网 的支持！
猜你喜欢:
本站部分资源来源于网络，本站转载出于传递更多信息之目的，版权归原作者或者来源机构所有，如转载稿涉及版权问题，请联系我们。
JSON 在线解析
在线 JSON 格式化工具
RGB HSV 转换
RGB HSV 互转工具